This session covers an overview of the progress and new scientific approaches for investigating landslides using state-of-the-art techniques such as: Earth Observation (EO), close-range Remote Sensing techniques (RS) and Geophysical Surveying (GS).
A series of remarkable technological progresses are driven new scientific opportunities to better understand landslide dynamics worldwide, including integrated information about rheological properties, water content, rate of deformation and time-varying changes of these parameters through seasonal changes and/or progressive slope damage.
This session welcomes innovative contributions and lessons learned from significant case studies and/or original methods aiming to increase our capability to detect, model and predict landslide processes at different scales, from site specific to regional studies, and over multiple dimensions (e.g. 2D, 3D and 4D).
A special emphasis is expected not only on the particularities of data collection from different platforms (e.g. satellite, aerial, UAV, Ground Based...) and locations (e.g. surface- and borehole-based geophysics) but also on new solutions for digesting and interpreting datasets of high spatiotemporal resolution, landslide characterization, monitoring, modelling, as well as their integration on real-time EWS, rapid mapping and other prevention and protection initiatives. Examples of previous submissions include using one or more of the following techniques: optical and radar sensors, new satellite constellations (including the emergence of the Sentinel-1A and 1B), Remotely Piloted Aircraft Systems (RPAS) / Unmanned Aerial Vehicles (UAVs) / drones, high spatial resolution airborne LiDAR missions, terrestrial LIDAR, Structure-from-Motion (SfM) photogrammetry, time-lapse cameras, multi-temporal DInSAR, GPS surveying, Seismic Reflection, Surface Waves Analysis, Geophysical Tomography (seismic and electrical), Seismic Ambient Vibrations, Acoustic Emissions, Electro-Magnetic surveys, low-cost sensors, commercial use of small satellites, Multi-Spectral images, etc. Other pioneering applications using big data treatment techniques, data-driven approaches and/or open code initiatives for investigating mass movements using the above-described techniques will also be very welcomed.
GUEST SPEAKER: this year, we invited professor Jonathan Chambers, team leader of the geophysical tomography cluster at the British Geological Survey (BGS).
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Chat time: Wednesday, 6 May 2020, 08:30–10:15
Landslides triggered by hydrological factors pose a risk to human safety and socioeconomic activities across the world. Detailed knowledge of the spatial extents of hydrogeological units in the landslide system, combined with an understanding of how moisture dynamics within these units vary over time, is crucial for identifying failure mechanisms and predicting future slope destabilisation. For landslide systems in which point-source monitoring information is sparse or depth-limited, spatially high-resolution time-lapse geophysical surveys can be used to both characterise the subsurface and infer changes in the saturation state in areas for which no point-source observations are available. Hence, geophysical characterisation and monitoring approaches can be used to improve local landslide early-warning systems, the majority of which predominantly rely on surface observations, or sparse subsurface data, to inform failure predictions.
Here, we present the results of an integrated geophysical characterisation and monitoring campaign undertaken at the Hollin Hill Landslide Observatory in North Yorkshire, UK. The observatory is situated in Lias Group mudrocks, comprising the failing clay-rich Whitby Mudstone Formation overlying the more stable Staithes Sandstone Formation. The landslide displays accelerated displacement during periods of high antecedent ground moisture and increased rainfall, driven by increased pore water pressures at the contact between the mudstone and sandstone. Over a period of 22 months, eleven co-located electrical resistivity tomography and seismic refraction tomography surveys were undertaken at the site. This campaign has the aim of characterising and monitoring the subsurface at resolutions and depths greater than exclusively using on-site surface or near-surface sensors (piezometers, moisture content and water potential sensors, etc.) or intrusive observations (boreholes, trial-pits, etc.).
Using a combined analysis of geoelectrical and seismic data, the subsurface of the landslide is discretised into hydrogeological units, which have distinct geoelectrical and seismic relationships corresponding to spatial variations in lithology and saturation. Variations in resistivity over time within these units are sensitive to changes in moisture content, and established site-specific petrophysical relationships between resistivity and moisture content are used to monitor the saturation state of the subsurface. Similarly, seismic derivatives, in particular P- to S-wave ratio and Poisson’s ratio, are sensitive to changes in elastic properties induced by increases in moisture, providing information on the volumetric changes of subsurface units in relation to changes in saturation. The integrated monitoring provided by these combined geoelectrical and seismic methods reveals relative spatiotemporal variations in material properties including saturation, shear strength and shrink-swell state, all of which are important when considering slope destabilisation. This study highlights the need for incorporating high-spatial resolution monitoring approaches for managing and mitigating future landslide failures, and underscores geophysical monitoring methods as a powerful tool to be included when providing early-warning of slope destabilisation.
How to cite: Whiteley, J., Uhlemann, S., Watlet, A., Boyd, J., Chambers, J., and Kendall, M.: Integrated time-lapse geophysical surveys for hydrogeological characterisation and monitoring of a clay-rich landslide in North Yorkshire, UK, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9082, https://doi.org/10.5194/egusphere-egu2020-9082, 2020.
Given a landslide, which are the constituent materials? How are the material inhomogeneities distributed? Which are their properties? What are the deformation processes? How large are the boundaries or how depth is/are the slip surface/s? Answering these questions is not a simple goal. Therefore, since the ‘70s, the international community (mainly geophysicists and lower geologists and geological engineers) has begun to employ, together with other techniques, active and passive geophysical methods to characterize and monitor landslides. Both the associated advantages and limitations have been highlighted over the years, but some drawbacks are still open.
On the basis of the more recent landslides classification by Hungr et al. dated 2014, an analysis of about 120 open access papers published in international journals between the 2007 and the 2018 has been carried out. The aim of this review work was to evaluate the geophysical community efforts in overcoming the geophysical technique limitations highlighted in the conclusion section of the review of 2007 by Jongmans and Garambois. These drawback can be summarized ad follow: 1) geophysicists have to make an effort in the presentation of their results; 2) the resolution and the penetration depth of each method are not systematically discussed in an understandable way; 3) the geological interpretation of geophysical data should be more clearly and critically explained; 4) the challenge for geophysicists is to convince geologists and engineers that 3D and 4D geophysical imaging techniques can be valuable tools for investigating and monitoring landslides; and 5) efforts should also be made towards obtaining quantitative information from geophysics in terms of geotechnical parameters and hydrological properties
Moreover, the review work highlighted that the most studied landslides are those of the flow type and fall type for the “soil” and “rock” category, respectively. From the “employed method” point of view, active and passive seismic methods are the most employed in landslide characterization and monitoring. The latest method is also able to remotely detect events that might otherwise go unnoticed for weeks or months, and therefore, it is widely employed. The three more frequently applied techniques to characterize and monitor the slope deformation are electrical resistivity tomography, seismic noise, and seismic refraction. Finally, the main conclusion is that independently of the applied technique/s or the landslide type, a very accurate and high-resolution survey could be performed only on a small landslide portion, as it is costly and time-consuming, even though geophysical techniques are defined as cost and time effective compared to traditional field methods.
How to cite: Morelli, S., Pazzi, V., and Fanti, R.: Landslides and Geophysics: a review of the advantages and limitations on the basis of the last twelve years open access international literature, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5236, https://doi.org/10.5194/egusphere-egu2020-5236, 2020.
Assessing the geometry and volume of mass movements is essential for the appraisal of slope stability and for the understanding of slope failure trigger mechanisms. For the latter, we developed seismic ambient noise measurement techniques in order to better characterize the sub-surface of ancient deep-seated landslides in seismic regions as the Carpathian Mountains in Romania.
In particular, we conducted thorough seismological and geophysical campaigns on the landslides of Eagle’s Lake, Paltineni, and Varlaam, in the Buzau-Vrancea region, Romania. This region, marked by a high seismicity with intermediate-depth earthquakes, hosts very large and generally old (i.e. >1000 years) mass movements with morphologies which might be due to seismically induced failure.
On the three study sites, we performed abundant horizontal-to-vertical noise spectral ratio (HVSR) measurements and installed several seismic arrays. The HVSR technique, based on the analysis of three component seismic signals, is commonly used to identify the resonance frequency of a given site. Polarization of the seismic wavefield is also investigated over the landslides. Through the installation of seismic arrays, we analyse the dispersive properties of the surface waves. By jointly inverting the information through a non-linear approach, we retrieve the shear-wave velocity profiles beneath the arrays and identify velocity contrasts with depth.
On Eagle’s Lake and Paltineni rockslides, the results have also been integrated with seismic refraction tomography profiles, evidencing lateral contrasts in soil properties, and multichannel analysis of surface waves providing the subsurface shear-wave velocities. At Varlaam, the extensive measurements performed over the landslide allowed us to identify a major impedance contrast at depth highlighting the base of the failed body. We also performed UAV flights to establish a 3D model of the investigated sites. All these investigations contributed to assess the landslide geometries and estimate the volumes of the failed bodies.
This work aims, in prospect, at reconstructing the conditions and the energy needed for triggering these landslides in order to understand if a seismic component is applicable in the failure process.
How to cite: Cauchie, L., Mreyen, A.-S., Cerfontaine, P., Micu, M., and Havenith, H.-B.: Investigation of ancient mass movements by seismic noise analysis: application to the Romanian Carpathian Mountains, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6975, https://doi.org/10.5194/egusphere-egu2020-6975, 2020.
Many regions of the world are exposed to landslides in clayey deposits, which pose major problems for land management and human safety. Clayey landslide activity is complex, showing a succession of periods of inactivity and reactivation phases that can evolve into sudden acceleration and catastrophic landslides and/or flows. Understanding the processes that control this activity therefore requires the continuous monitoring of specific parameters. At the end of June 2016, the Harmalière clayey landslide (located 30 km south of the city of Grenoble in the French Alps) was dramatically reactivated at the headscarp after 35 years of continuous but limited activity. The total volume involved, which moved in the form of tilted blocks of different sizes, was estimated at about 3,106 m3. Several sensors, including seismometers and GNSS stations, were installed immediately behind the main escarpment in early August 2016. They recorded a rupture involving a block of a few hundred cubic meters in November 2016. Additional data (seismology, meteorology, piezometer, etc.) were provided by a permanent observatory located a few hundred meters away in the nearby Avignonet landslide (RESIF2006). Two three-component seismic sensors were placed on the collapsed block and 10 meters aft on the stable part respectively.
Thus, four seismic parameters were monitored for 4 months until the clay block rupture: the cumulative number of microseisms, the resonance frequency of the block, the relative variation in Rayleigh wave velocity (dV/V) and the correlation coefficient (CC) in the range 1-12 Hz. All these parameters showed a significant precursor signal before the rupture, but at very different times. During the monitoring period, they also showed different responses to environmental parameters and in particular to precipitation. The resonance frequency increased slightly but steadily from 8 to 9 Hz (+12%) during the pre-break period, then decreased from 9 Hz to 7 Hz (-22%) just one hour before the break. However, the other three parameters showed significant variations a few weeks before failure. The dV/V and CC parameters reacted 1.5 month before the failure, during a very heavy rain event. The CC showed a general decrease over time, first affecting the high frequencies, then gradually spreading to the low frequencies. Finally, seismic activity is almost constant during the first three months, with only slight temporary increases during precipitation. One month before the rupture, a significant increase in the number of events is observed.
This study shows the potential of monitoring different seismic parameters over time in order to predict the slip of blocks in a clay material.
How to cite: Fiolleau, S., Jongmans, D., Bièvre, G., Chambon, G., and Baillet, L.: Seismic characterization of clays blocks ruptures in a clayey landslide, the Harmaliere landslide., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7308, https://doi.org/10.5194/egusphere-egu2020-7308, 2020.
Billions of passive radiofrequency tags are produced by the Radio-Frequency Identification (RFID) industry every year to identify goods remotely. New research and business applications are continuously arising, including recently localization and sensing for earth science. Indeed, the cost of tags is often several orders of magnitudes below conventional outdoor sensors used in earth science, allowing to deploy up to thousands of tags with minimal investment. Furthermore, passive wireless tags require little maintenance, which fits well for years-long monitoring. This study reviews the earth science applications that are being developed today, that use RFID devices available on the market, i.e., 900 MHz far-field tags and 125 kHz near-field tags.
Ground displacements of centimeters to hundreds of meters can be monitored using RFID location techniques. Indeed, RFID tags were firstly used in earth science to track the displacement of riverine and coastal sediments due to bedloading. Near-field tags inserted in pebbles can be identified typically up to 0.5 m from the reading device even when buried. The tags are read either by fixed portals or by a mobile device, obtaining either high space or time resolution data, respectively. Very recently, measuring the phase difference of arrival of far-field tags allowed to estimate displacements with centimetric accuracy, with a tag-reader distance up to 50 m. That allowed measuring the ground displacements continuously relatively to a fixed reader, or to estimate tags location placed on the ground by carrying a reader over a drone using the synthetic aperture radar method. Alternatively, RFID tags can also be used for sensing the evolution over time of the temperature, moisture level, vibrations, resonant frequency or crack opening of a geologic object.
This review presents multiple applications for monitoring unstable rock/earth structures using RFID. First, slow landslides can be monitored with accurate displacement monitoring and with soil moisture sensors. Then, prone-to-failure rock columns could be monitored by sensing crack opening or resonant frequency, using the same tags as with the concrete structure applications. Finally, sediment loading due to rapid mass movements such as floods, debris flows, tsunami or typhoons, have been studied largely using tags placed into pebbles.
Author’s published work on the topic:
- Le Breton, M., Baillet, L., Larose, E., Rey, E., Benech, P., Jongmans, D., Guyoton, F., 2017. Outdoor UHF RFID: Phase Stabilization for Real-World Applications. IEEE Journal of Radio Frequency Identification 1, 279–290.
- Le Breton, M., Baillet, L., Larose, E., Rey, E., Benech, P., Jongmans, D., Guyoton, F., Jaboyedoff, M., 2019. Passive radio-frequency identification ranging, a dense and weather-robust technique for landslide displacement monitoring. Engineering Geology 250, 1–10.
- Le Breton, M., 2019. Suivi temporel d’un glissement de terrain à l’aide d’étiquettes RFID passives, couplé à l’observation de pluviométrie et de bruit sismique ambiant (PhD Thesis). Université Grenoble Alpes, ISTerre, Grenoble, France.
How to cite: Le Breton, M., Baillet, L., Larose, É., Rey, E., Jongmans, D., Guyoton, F., and Benech, P.: Passive RFID, a new technology for dense and long-term monitoring of unstable structures: review and prospective., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19726, https://doi.org/10.5194/egusphere-egu2020-19726, 2020.
Landslide hazard has always been a significant source of economic losses and fatalities in the mountainous regions. Knowledge of the spatial extent of the past and present landslide activity, compiled in the form of a landslide inventory map, is essential for effective risk management. High-resolution data acquired by Earth observation (EO) satellites are often used to map landslides by identifying morphological expressions that can be associated with past and/or recent deformation. This is a slow and difficult process as it requires extensive manual efforts. As a result, such maps are not readily available for all the landslide hazard affected regions. Fully automated methods are required to exploit the exponentially increasing amount of EO data available for landslide hazard assessments. In this context, conventional methods like pixel-based and object-based machine learning strategies have been studied extensively in the last decade. Recent advances in convolutional neural network (CNN), a type of deep-learning method, has outperformed other conventional learning methods in similar image interpretation tasks. In this work, we present a deep-learning based method for semantic segmentation of landslides from EO images. We present the results from a study area in the south of Portland in Oregon, USA. The landslide inventory for training and ground truth was extracted from the Statewide Landslide Information Database of Oregon (SLIDO). We were able to achieve a probability of detection (POD) greater than 0.70. This method can also be extended to be used for rapid mapping of landslides after a major triggering event (like earthquake or extreme metrological event) has occurred.
This work is done in the framework of European Commission's Horizon 2020 project "BETTER”. More information is available on the website https://www.ec-better.eu/.
How to cite: Prakash, N., Manconi, A., and Loew, S.: Mapping landslides from EO data using deep-learning methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11876, https://doi.org/10.5194/egusphere-egu2020-11876, 2020.
The recent development of mobile surveying platforms and crowd-sourced information has produced a huge amount of non-validated data
that are now available for research. In the field of landscape analysis, with particular reference to geomorphology and engineering geology, images generated by autonomous platforms (such as UAVs, ground-based acquisition systems, satellite sensors) and pictures obtained from web data-mining can be easily gathered and contribute to the fast surge in the amount of non-organised information that engulf data storage facilities. The high potential impact of such methods, however, may be severely impacted by the need of a massive amount of Human Intelligent Tasks (HIT), which is necessary to filter and classify the data, whatever the final purpose.
In landslide hazard analysis, both UAV-surveys and the gathering of crowd-sourced information generate big-data that would require HITs before becoming usable in early warning, vulnerability assessment, residual risk estimation, model parametrisation and mapping. Very often, this an important limitation to the real-world applications that are actually feasible with the support of such systems. Examples of such HITs are the intelligent guidance of drones, the classification of fake news, the validation of post-disaster information.
Computer vision can be of great help in fostering the autonomous capability of intelligent systems to complement, or completely substitute, HITs. Image and object recognition are at the forefront of this research field. They are based on a number of computer-aided methods that rely on different degrees of interaction with the user, ranging from semi-automated object-based detection to deep learning by neural networks.
In this work, we present a new set of convolutional neural networks specifically designed for the automated recognition of landslides and mass movements in non-standard pictures that can be used for supporting UAV automated guidance and data-mining filtering. The deep learning has been accomplished by resorting to transfer learning of some of the top-performers CNNs available in the literature. Results show that the deep learning machines, calibrated on a relevant dataset of validated images of landforms, are able to supply reliable predictions with computational time and resource requirements compatible with most of the UAV platforms and web data-mining applications for landslide hazard studies.
How to cite: Catani, F.: Landslide recognition by deep learning of non-standard multi-source images, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19477, https://doi.org/10.5194/egusphere-egu2020-19477, 2020.
Slow-moving, deep-seated landslides travel downslope at rates of only a few meters per year and can remain active for decades and possibly centuries. As a result, they transmit large quantities of sediment to the channel network and are a major natural hazard that impact transport corridors and infrastructure. However, because slow-moving landslides rarely fail catastrophically, it is challenging, and often infeasible to directly measure their thickness and volume, two key parameters required to quantify sediment flux and to model landslide motion. Here we use remote sensing data from the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) to measure the 3-D surface velocity and geometry of over 90 slow-moving landslides in the California Coast Ranges. We then use mass conservation techniques to infer the thickness and volume of each landslide. These landslides have volumes that span between 104 and 107 m3, thicknesses between 3 and 90 m, and move at average annual rates < 5 m/yr. We also examined landslide depth-area and volume-area geometric scaling relations and compared our findings to a worldwide inventory of soil and bedrock landslides compiled by Larsen et al. (2010). We find that the landslide thickness, area, and volume are larger than soil landslides and smaller than bedrock landslides globally. Lastly, we estimate the subsurface geometry of the catastrophic Mud Creek landslide, central California Coast Ranges, during a period of slow motion that lasted at least 8 years before its ultimate failure. We find a volume of ~2.0 x 106 m3, which is close to the post-catastrophic failure volume measured using Structure From Motion (~2.1 x 106 m3) by Warrick et al. (2019). Therefore, in certain cases, it is possible to constrain landslide thickness and volume prior to catastrophic collapse. Our work shows how state-of-the-art remote sensing techniques can be used to better understand landslide processes and quantify their contribution to landscape evolution.
How to cite: Handwerger, A., Fielding, E., Booth, A., and Huang, M.-H.: Depth, area, volume, and kinematics of slow-moving landslides from airborne synthetic aperture radar and mass conservation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12676, https://doi.org/10.5194/egusphere-egu2020-12676, 2020.
Landslides represent a worldwide natural hazard and often occur as cascading effects related to triggering events, such as earthquakes and hydrometeorological extremes. Recent examples are the Kaikoura earthquake in New Zealand (November 2016), the Gorkha earthquake in Nepal (April/May 2015), and the Typhoon Morakot in Taiwan (August 2009) as well as less intense rainfall events persisting over unusually long periods of time as observed for Central Asia (spring 2017) and Iran (spring 2019). Each of these events has caused thousands of landslides that account substantially to the primary disaster’s impact. Moreover, their initial failure usually represents the onset of long-term progressing slope destabilization leading to multiple reactivations and thus to long-term increased hazard and risk. Therefore, regular systematic high-resolution monitoring of landslide prone regions is of key importance for characterization, understanding and modelling of spatiotemporal landslide evolution in the context of different triggering and predisposing settings. Because of the large extent of the affected areas of up to several ten thousands km2, the use of multi-temporal and multi-scale remote sensing methods is of key importance for large area process analysis. In this context, new opportunities have opened up with the increasing availability of satellite remote sensing data of suitable spatial and temporal resolution (Sentinels, Planet) as well as the advances in UAV based very high resolution monitoring and mapping.
During the last decade, we have been pursuing extensive methodological developments in remote sensing based time series analysis including optical and radar observations with the goal of performing large area and at the same time detailed spatiotemporal analysis of landslide prone regions. These developments include automated post-failure landslide detection and mapping as well as assessment of the kinematics of pre- and post-failure slope evolution. Our combined optical and radar remote sensing approaches aim at an improved understanding of spatiotemporal dynamics and complexities related to evolution of landslide prone slopes at different spatial and temporal scales. In this context, we additionally integrate UAV-based observation for deriving volumetric changes also related to globally available DEM products, such as SRTM and ALOS.
We present results for selected settings comprising large area co-seismic landslide occurrence related to the Kaikoura 2016 and the Nepal 2015 earthquakes. For the latter one we also analyzed annual pre- and post-seismic monsoon related landslide activity contributing to a better understanding of the interplay between these main triggering factors. Moreover, we report on ten years of large area systematic landslide monitoring in Southern Kyrgyzstan resulting in a multi-temporal regional landslide inventory of so far unprecedented spatiotemporal detail and completeness forming the basis for further analysis of the obtained landslide concentration patterns. We also present first results of our analysis of landslides triggered by intense rainfall and flood events in spring of 2019 in the North of Iran. We conclude that in all cases, the obtained results are crucial for improved landslide prediction and reduction of future landslide impact. Thus, our methodological developments represent an important contribution towards improved hazard and risk assessment as well as rapid mapping and early warning
How to cite: Roessner, S., Behling, R., Motagh, M., and Ulrich-wetzel, H.: Multi-scale analysis of landslide occurrence and evolution using optical and radar time series, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20702, https://doi.org/10.5194/egusphere-egu2020-20702, 2020.
The InSAR technique has been proved to be a powerful tool in order to detect, monitoring and analyse movements related to geological phenomena. Its application ranges from regional/national scale to a very detailed scale, up to a single building analysis. Moreover, since 2014, the free and constant availability of Sentinel-1 data has been helping the tendency of using more and more this technique in the institutional risk management activities. Many European and national projects have been financed in order to investigate and improve the processing performances and broaden the operational use and application of the results. In this work, we present the first results developed in the framework of the project Riskcoast (SOE3/P4/E0868) over an area of around 4 km2 in Andalucía (Spain), including the city and the coast of Granada. Riskcoast has been funded by the Interreg Sudoe Programme through the European Regional Development Fund (ERDF). The presented work is as an example of multi scale (medium to large) application of InSAR for geohazard applications. The velocity map including the estimation of the displacement time series have been produced over the whole area by processing 139 radar images of the Sentinel-1 (A and B). Starting from those results a rapid and semi-automatic extraction of the most significant active displacement areas (ADA) has been performed. Then, after a classification of the detected areas, a more detailed analysis has been done over some selected costal landslides. Over those landslides a damage mapping has been generated based on field surveys, and then analysed together with the spatial gradient of displacement derived by the InSAR results. The Riskcoast project will be introduced and the first results presented.
How to cite: Barra, A., Reyes-Carmona, C., Monserrat, O., Glave, J. P., Herrera, G., Mateos, R. M., Sarro, R., Bejar, M., Azañón, J. M., and Crosetto, M.: Sentinel-1 for Granada coast landslides monitoring and potential damage assessment , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19236, https://doi.org/10.5194/egusphere-egu2020-19236, 2020.
Landslides and floods driven by typhoon and monsoon rainfall cause thousands of fatalities and millions of pesos in damage to infrastructure and commerce in the Philippines each year. The Philippines accounts for 46% of rainfall-triggered landslides in SE Asia, although it represents only 6% of the land area (Petley, 2012).
Despite their relevance, landslide inventories are very scarce in the Philippines, and most of them are point-based inventories, so lacking landslide magnitude. This makes it difficult both to assess their magnitude-frequency relationships (major component of hazard assessment) and to provide landslide sediment delivery rates to the river network (needed for better prediction of channel morphodynamics, flood risk and reservoir management), which is one of the main goals of the SCaRP project (Simulating Cascading Rainfall-triggered landslide hazards in the Philippines), funded under Newton Programme (UK Research and Innovation).
Manually mapping landslides to obtain polygon-based landslide inventories in areas affected by RILs (Rainfall Induced regional Landslide events) is a time-consuming task, which is often not affordable for the authorities in terms of resources and time. Meanwhile, automatic methods to map landslides based on satellite imagery have broadly improved during the last decade (e.g.: Alvioli et al 2018).
The city of Itogon (Benguet, Luzon) and its surroundings was hit by typhoon Mangkhut in September 2018, which triggered thousands of landslides, including a fatal one that killed over 70 miners. We selected a test area of 135 km2, with a high density of landslides.
The objective of this work was twofold: 1) to characterize the geomorphological features of the landslides that occurred in the area of Itogon due to the passage of Typhoon Mangkhut, 2) to analyze the potential of automatic tools to map landslides from satellite imagery.
A total number of 1100 shallow landslides and flows were manually mapped, with areas ranging from tens to tens of thousands of m2. An automatic pixel-based approach (developed within H2020 HEIMDALL project and called Slidex) was tested, which relies on a Random Forest classification using Sentinel-2 bands and a set of radiometric indices. The algorithm was trained over several regions (e.g. Japan, Sierra Leone) and applied to the Philippines. The results suggest that the change in land cover is the best indicator to identify landslides automatically, though the efficiency of the tool was improved by including geomorphological parameters such as slope and minimum area affected.
Alvioli, M., Mondini, A. C., Fiorucci, F., Cardinali, M., & Marchesini, I. (2018). Topography-driven satellite imagery analysis for landslide mapping. Geomatics, Natural Hazards and Risk, 9(1), 544–567. https://doi.org/10.1080/19475705.2018.1458050
Petley, D. (2012) Global patterns of loss of life from landslides. Geology, 40(10), 927-930
How to cite: Abancó, C., Bennett, G., Briant, J., and Battiston, S.: Towards an automatic landslide mapping tool based on satellite imagery and geomorphological parameters. A study of the Itogon area (Philippines) after Typhoon Mangkhut, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17940, https://doi.org/10.5194/egusphere-egu2020-17940, 2020.
Several geodetic methods can be combined to better understand landslide dynamics and behavior. The obtained deformation/displacement fields can be analyzed to inverse the geometry of the moving mass and the mechanical behavior of the slope (kinematic regime, rheological properties of the media), and sometimes anticipate the time of failure. Among them, dense in-situ measurements (total station measurements, extensometer data and GNSS surveys) allow reaching accuracy close to the centimeter. These techniques can be combined to dense time series of passive terrestrial imagery in order to obtain distributed information. Actually, more and more passive optical sensors are used to provide both qualitative information (detection of surface change) and quantitative information using either a single camera (quantification of displacement by correlation techniques) or stereo-views (creation of Digital Surface Models, DSM).
In this study, we analyze a unique dataset of the Cliets rockslide event that occurred on 9 February 2019. The pre-failure and failure stages were documented using the above mentioned methods. The performance of the methods are evaluated in terms of their possible contribution to a monitoring survey.
The Cliets landslide is located in the French Alps (Savoie) and is affecting the high traffic road of Gorges de l’Arly. Located upstream of a tunnel, the unstable slope was instrumented by the SAGE Society during the crisis in the period July–February 2019. About 8000 m3 collapsed closing the tunnel access for one year. Topographic measurements of a series of 41 benchmarks by automated total station were used to determined the time of rupture and the landslide mechanical behavior (tertiary creep vs stable regime). Additionally, a fixed CANON EOS 2000D with a lens with a focal length of 24 mm, was installed in front of the landslide. Images were acquired hourly and the time series was processed using the TSM processing toolbox (Desrues et al., 2019). Displacement fields were generated over time and compared to the topographic measurements. Photogrammetric surveys were carried out to generate several DSMs before and after the crisis. It allowed to estimate the volume of the collapsed masses. Finally, geophysical surveys were included in the study to determine the thickness of the potential unstable layer.
The results allow highlighting (1) different kind of behaviors which are identified and explained by a simple physical models, (2) the volumes of the displaced masses, and (3) the absence of a direct relation of the failure with the meterological forcing factors.
Acknowledgments: These works are part of a CIFRE / ANRT agreement between IPGS/CNRS UMR7516 and the SAGE Society.
How to cite: Desrues, M., Malet, J.-P., Brenguier, O., Carrier, A., and Lorier, L.: Landslide dynamics inferred from in-situ measurements and time series of terrestrial imagery: the Cliets rockslide (Savoie, French Alps), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11197, https://doi.org/10.5194/egusphere-egu2020-11197, 2020.
Rock glaciers are geomorphological phenomena of mountain permafrost which slowly move downslope as a consequence of the ice deformation. During the last few decades, many rock glaciers in the Alps are showing an increase of flow velocities which is most probably caused by climate change. However, the factors influencing the flow velocities (e.g. air temperature, meltwater infiltration, internal rock glacier characteristics) are not fully understood. Data about the annual, inter-annual and diurnal rock glacier flow velocities are essential to understand the influence of climatic factors on rock glaciers.
This study focused on the Finstertal rock glacier, located in the Eastern Alps, where flow velocities are reconstructed since the 1970s based on aerial imagery, airborne and terrestrial laser scan data. Since 2014, a terrestrial laser scanning (TLS) based monitoring is implemented. The maximum flow velocities of the Finstertal rock glacier increased from 0.1 m/year (time period 1970-1997) to 1.4 m/year (time period 2015-2016) and is currently about 1.3 m/ year (time period 2018-2019).
The accuracy of aerial imagery and laser scan data is in the range of centimetres and well suited to analyse the annual variability of rock glaciers. Imagery and laser scan data are not suited for shorter time intervals, where the absolute displacement of a rock glacier is smaller than the measurement accuracy. Consequently, for the understanding of interannual and diurnal variations in rock glacier flow velocities, other measurement methods are needed. Ground-based interferometric synthetic aperture radar (GBInSAR) is able to detect spatial deformations in the range of sub-centimeters.
Therefore, to get a more detailed understanding of the rock glacier flow velocity variations, a GBInSAR was installed on Finstertal hydroelectric dam to measure the rock glacier flow velocities between October to November 2019. In this study, preliminary results on diurnal flow velocity variations of Finstertal rock glacier, based on GBInSAR, are presented, and compared to annual variations derived from aerial imagery and laser scan data.
How to cite: Fey, C., Kuschel, E., Amabile, A. S., Straka, W., and Zangerl, C.: Monitoring of rock glacier flow velocity variations using imagery, laser scan data and ground-based interferometric synthetic aperture radar (GBInSAR) at the Finstertal reservoir (Austria), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10451, https://doi.org/10.5194/egusphere-egu2020-10451, 2020.
We assess the use of novel geophysical monitoring approaches to spatially characterise geotechnical properties and processes driving slope failure, and consider the contribution of geophysical technologies to the development of slope-scale early warning systems (EWS). In particular, we focus on geoelectrical monitoring approaches to image moisture driven processes, supported by the use of shallow seismic surveys to illuminate elastic property distributions and changes. We describe an approach for using spatial and volumetric geophysical models of slope structures and processes to better inform geotechnical models of slope stability and estimates of factor of safety.
Key components of the approach have included: automated schemes and instrumentation for measuring and processing field-scale time-lapse geophysical and geotechnical data sets; laboratory based assessments of geophysical-geotechnical property relationships (e.g. between resistivity, moisture content and pore suctions) to aid the interpretation of slope-scale geophysical models; and linked geophysical-geomechanical modelling to provide near-real-time estimates of slope stability to aid forecasting of landslide events. Our approach is illustrated with results from a range of field sites located on natural and engineered slopes. We conclude that the spatially rich subsurface information provided by geophysical monitoring can make a substantial contribution to landslide EWS and can provide an improved understanding of the condition of unstable slopes.
How to cite: Chambers, J. and the Landslide Geophysics Consortium: Long-term geophysical-geotechnical monitoring of landslide processes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13118, https://doi.org/10.5194/egusphere-egu2020-13118, 2020.
Electrical Restivity Tomography (ERT) is one of the most employed geophysical technique to monitor landslide evolution. The measured variations of resistivity can be related to changes in underground moisture, porosity, water chemistry, etc. With electrodes installed on the moving mass, resistivity variations may also be related to changes in electrode location relative to each other (the so-called geometric factor K). As such, ERT monitoring should also require the monitoring of electrode location. Wilkinson et al. (2010, 2015) were able to track movements of electrodes by measuring variations of resistivity. However, this approach needs the strong assumption that resistivity variations are caused by changes in the geometric factor without any underground change. For example, Gance et al. (2015) showed the significant effect of surface fissures on ERT measurements.
In this work we tested ERT monitoring of an earthslide (the Pont-Bourquin Landslide in the Swiss Alps) with electrodes located immediately outside the unstable zone. The setup was composed of 36 electrodes (24 on the right bank and 12 on the left bank) acquiring 1654 measurements per day in a dipole-dipole configuration (half direct and half reciprocal measurements). 235 daily sequences were acquired between February and November 2015. Data were filtered and then processed with the BERT package (Günther et al., 2006). Several time-lapse approaches were tested with different starting models originating from the 3D inversion of 4, 2D profiles and the results were analyzed in terms of resistivity and sensitivity variations. The resulting 3D models were then split in distinct zones (transport and accumulation zones) and the ERT time-series were then correlated with environmental time-series (e.g. rainfall).
Results indicate that, despite a lack of sensitivity in the unstable zone because of the monitoring set-up, ERT is sensitive to environmental variations but no distinct behaviour could be observed within the zones. However, correlations provide informations in agreement with passive seismic monitoring (Bièvre et al., 2018) and suggest that resistivity (along with shear wave velocity) is strongly affected by rainfall with an effect that does not last more than 2 to 3 days. These results confirm that the superficial layers (first metres) have a major influence on resistivity measurements. More generally these results, along with many published works, question the added value of ERT to monitor landslides for depths greater than the superficial phreatic water table.
Bièvre G et al. (2018) Eng. Geol. 245, 248 - 257. doi:10.1016/j.enggeo.2018.08.01
Gance J et al. (2015) Geophy. J. Int. 200, 1118-1135. doi:10.1093/gji/ggu453
Günther T et al. (2006) Geophy. J. Int. 166, 506-517. doi:10.1111/j.1365-246X.2006.03011.x
Wilkinson P B et al. (2010) Geophy. J. Int. 183, 543-556. doi:10.1111/j.1365-246X.2010.04760.x
Wilkinson P B et al. (2015) Geophy. J. Int. 200, 1566-1581. doi:10.1093/gji/ggu483
How to cite: Bièvre, G., Jongmans, D., Lebourg, T., and Carrière, S.: Electrical resistivity monitoring of an earthslide with electrodes located outside the unstable zone (Pont-Bourquin landslide, Swiss Alps), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9022, https://doi.org/10.5194/egusphere-egu2020-9022, 2020.
As the infrasound signal has the advantages of slow energy attenuation, strong ability to cross obstacles and no need of contact acquisition, it is of great significance to take advantage of the infrasound signal in the process of deformation and failure of rock and soil mass to realize remote rapid monitoring and early warning of geological disasters. The infrasound signal characteristics of soil slope failure and rock under different stress states (compression, shear and tension) were compared by indoor and outdoor tests. The results showed that there was an obvious waveform of infrasound signal at the site of soil slope damage. The infrasound signal appeared mainly in elastic and plastic deformation stages under the compression state, the peak frequency of the infrasound signal was about 7 Hz. The concentration of signal power was slightly less than that under the compression state, and the peak frequency was about 8 Hz. The infrasound signal always associated with the whole loading process under tension state, and there were two bands of frequency center, in which the lower frequency was close to that of the compression test specimen, and the higher frequency was 3 Hz larger. On this basis, using the infrasound characteristics of rock and soil mass failure, the infrasound and other monitoring methods were carried out for Xinpu landslide in Fengjie, Chongqing, China. After the occurrence of the landslide, the infrasound signal characteristics of rock and soil failure were basically the same as those of indoor tests. The low-frequency signals were mainly monitored. At the same time, the monitoring results showed that the peak value of the infrasound signal reached before the mechanical signal, and the mechanical signal was monitored prior to the displacement signal. The infrasound signal can be 3-5 hours ahead of displacement signal. This regularity has important scientific and application value for landslide monitoring and prediction.
How to cite: Fu, X., Ban, Y., Xie, Q., and He, C.: Infrasound signal characteristics of rock soil landslide and experience with its engineering application, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18211, https://doi.org/10.5194/egusphere-egu2020-18211, 2020.
The study presents the results of seismic measurements on the Just-Tegoborze landslide located in Outer Carpathians in the southern region of Poland. The aim of the study was to investigate the landslide geological subsurface and define S-wave velocity changes within geological medium using passive seismic interferometry (SI) and active multichannel analysis of surface waves (MASW). Additionally, seismic refraction and numerical slip surface calculations were carried out in order to combine the results.
Measurements of SI were conducted based on local high-frequency seismic noise generated by heavy vehicles passing state road which intersects Just-Tegoborze landslide. Seismic noise registration was made using three-component broadband seismometers installed along a seismic profile. Measurements were repeated in a few series in different season and hydration conditions.
Seismic sections show different velocity layers within the landslide medium. Comparing them with geological cross-section of the studied area, we can distinguish the main lithological boundaries. First near-surface seismic layers may correspond to clayey colluvium and clayey-rock colluvium. The deepest seismic layer probably correlates to less weathered flysch bedrock made of shales and sandstones. It can be identified as the main slip surface of the studied landslide.
S-wave velocities within seismic profiles significantly varies between each measurement series of SI. It can be observed a decrease of S-wave velocity in March and July which is connected to seasonal weather and hydration conditions. Strong increase of hydration during melting snow cover in March and after heavy rainfalls in July resulted in loss of rigidity what presumably led to drop of S-wave velocity. Changes in hydration could also cause the variation of the course of the less weathered flysch bedrock boundary.
Presented results of passive seismic interferometry measurements show that study of seismic noise can be applicable to subsurface identification of an active landslide. The example of Just-Tegoborze site indicates that based on seismic interferometry it is possible to observe changes in elastic properties of geological medium. It is worth to underline that SI and MASW complement each other in retrieving the information of Rayleigh surface wave. Combining the results with seismic refraction and numerical calculations allows to better image the landslide geological subsurface. Such observations may be helpful in assessing landslide threat.
How to cite: Harba, P. and Krawiec, K.: Application of passive and active seismic methods to subsurface investigation of Just-Tegoborze landslide (Outer Carpathians, Poland), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12652, https://doi.org/10.5194/egusphere-egu2020-12652, 2020.
The Preonzo rock slope instability in southern Switzerland partly collapsed in 2012, releasing a volume of ~210’000 m3 and leaving behind an unstable rock mass of about 140’000 m3. Shortly after the collapse, a small-aperture seismic array measurement was performed on the remaining unstable volume. The analysis of these data showed a fundamental resonance frequency of about 3.5 Hz and strong wavefield amplifications with factors of more than 30 in direction perpendicular to open tension cracks. Normal mode analysis by frequency domain decomposition using the fundamental and several higher modes allowed for mapping the fracture network of the instability.
However, the observed amplification factors and mode shapes could not be explained solely by the open tension cracks visible at the surface. Strong amplifications, especially at frequencies of higher modes, were observed on the uphill part of the rear fracture, which was supposed to be outside the presumed unstable area. The zone where amplifications rapidly decreased in the uphill direction coincides roughly with a geomorphological lineament in the field, interpreted as an additional, but hidden, rear fracture.
We performed active seismic refraction tomography across this lineament and discovered distinct low velocity anomalies in the transition zone from high to low amplifications, supporting the interpretation of an additional fracture. Considering this new finding, the volume of the unstable rock mass increases by about 40 %.
How to cite: Häusler, M., Glüer, F., Burjánek, J., and Fäh, D.: Chasing a hidden fracture using seismic refraction tomography: case study Preonzo, Switzerland , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9701, https://doi.org/10.5194/egusphere-egu2020-9701, 2020.
Seismology allows continuous recording of the activity of gravitational instabilities whatever the context, and is therefore able to provide a tool for the study of the spatio-temporal evolution of the activity of gravity instabilities with a unique resolution. Due to the considerable fall in the costs of the means of acquiring seismological data and the increasing densification of global, regional and local networks observed in recent years, the amount of data to be processed is growing exponentially. Thus access to information is more and more complete but in return the volume of data to be processed becomes considerable. To analyze this volume of data and extract relevant information, it is necessary to develop automatic methods of identification of seismic sources and location to quickly build the most complete seismicity catalogs possible.
We present a new machine-learning based method for automatically constructing catalogs of gravitational seismogenic events from continuous seismic data. We have developed a robust and versatile solution, which can be implemented in any context where seismic detection of landslides or other mass movements is relevant. The method is based on spectral detection of seismic signals and the identification of sources with a machine learning algorithm. Spectral detection detects signals with a low signal-to-noise ratio, while the Random Forest algorithm achieves a high rate of positive identification of seismic signals generated by landslides and other seismic sources. The processing chain is implemented to operate in parallel in a high-performance data center, which allows years of continuous seismic data to be explored and a database of events to be rapidly built up. This solution is also deployed for near-real time seismicity catalogs construction in the framework of slow moving landslides monitoring done by the Observatoire Multidisciplinaire des Instabilités de Versants (OMIV). Here we present the preliminary results of the application of this processing chain in different contexts, locally for the monitoring of slow-moving landslides (La Clapière, Super-Sauze, Séchilienne), and at the regional level for the detection of large landslides field (Alaska and Alps).
How to cite: Hibert, C., Malet, J.-P., Radiguet, M., Pillot, Q., Michéa, D., Provost, F., and Helmstetter, A.: Towards instrumental catalogs of gravitational instabilities at local and regional scales by a combined seismology and machine learning approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13796, https://doi.org/10.5194/egusphere-egu2020-13796, 2020.
The morphological variations of unstable areas can be computed using different methodologies that allow performing repeated surveys over time: aerial digital photogrammetry, aerial and terrestrial laser scanning, Synthetic Aperture Radar (SAR) satellites, terrestrial data, and GNSS observations in addition to the classical topographic applications.
In this work, the displacements of the Patigno landslide, a deep-seated gravitational slope deformation located in the Northern Apennine (Tuscany, Italy), are evaluated using archival aerial photogrammetry, continuous GNSS observations and multi-temporal SAR satellite data. In particular, the aerial photogrammetric surveys carried out in 1975 (scale 1:13000), 1987 (scale 1:13000), 2004 (scale 1:30000), 2010 (scale 1:10000), and 2013 (scale 1:30000) were analysed. These images have been processed using Socet Set software, in order to estimate the movements of several ground points on the study area. After the extraction of the photogrammetric models, the common reference system was verified by measuring checkpoints in the multi-temporal series located outside the deformation area, choosing well defined artificial points (mainly corners of buildings). Starting from the stereoscopic models, 5 automatic DEMs were extracted with 5 m grid step on the area that included the landslide and its surroundings: from the DEMs it was possible to obtain the corresponding orthophotos; thanks to the good visibility over the whole landslide area in the 1975 model, a DTM was obtained adapting the contour level to the real terrain morphology by means of stereoscopic devices. On the photogrammetric models, the approaches based on the measurements of homologous points in the multi-temporal dataset was adopted: 165 natural points were identified and measured in stereoscopy on each model (mainly corners of buildings); from the comparison of the 3D coordinates, displacement vectors in the four periods 1975-1987, 1987-2004, 2004-2010 and 2010-2013 were obtained. Due to the vegetation cover, the points were measured almost exclusively in the built-up areas of the Patigno, Noce and Val di Termine villages and, to a limited extent, on isolated buildings.
The interferometric data acquired by the Sentinel-1A/B satellites from 22-March-2015 to 18-May-2019, and the GNSS data acquired by a continuous station located in the central sector of the landslide (2004/01/01- 2018/12/31) were also analyzed. The GNSS data have been processed with GAMIT/GLOBK and RTKLib software.
The results obtained with the three different techniques will be presented along with the estimation of the spatial and temporal evolution of the landslide movement. The area where the continuous GNSS station is located moves with a velocity of about 3 cm/yr, along the direction of maximum slope, in accordance with the displacement rates measured with the photogrammetric and SAR data analysis.
How to cite: Cenni, N., Fiaschi, S., and Fabris, M.: Patigno landslide monitoring by the integration of multi-temporal observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4737, https://doi.org/10.5194/egusphere-egu2020-4737, 2020.
Chat time: Wednesday, 6 May 2020, 10:45–12:30
Abstract: Landslides are one of the most common and devastating natural hazards worldwide, which cause injuries to life and damage to properties, infrastructures leading to high-cost maintenance. In this study frequency ratio, information value and fuzzy logic models were used for landslide susceptibility mapping of an area of 356km2 in and around Dharamshala, Himachal Pradesh, using earth observation data. Dharamshala, a part of North-western Himalaya, is one of the fastest-growing tourism hubs with a total population of 30,764 according to the 2011 census and is amongst one of the hundred Indian cities to be developed as a smart city under PM’s Smart Cities Mission. The thrust for infrastructure development has led to a need for prior planning to minimize the consequences of landslide hazards. The final produced landslide susceptibility zonation maps with better accuracy could be used for land-use planning to prevent future losses. A landslide inventory for the study area was prepared through visual interpretation of high-resolution satellite imagery and available inventory report. Remote sensing data and other ancillary data like geological data were collected and processed in the GIS environment to generate thematic maps of parameters influencing landslide occurrence. The landslide causative parameters used in the study are slope angle, slope aspect, elevation, curvature, topographic wetness index, relative relief, distance from lineaments, land use land cover, and geology. Using these parameters and landslide inventory weight and membership value was calculated for the Frequency ratio, information value and Fuzzy logic model, respectively. In the frequency ratio and information value model, all the landslide causative parameters were arithmetically overlaid using calculated weights for landslide susceptibility mapping. In the fuzzy logic model, different fuzzy operators were applied to the calculated fuzzy membership values. Unlike the normalization process for membership calculation present study used the cosine amplitude method, which will give more reliable results. A total of ten landslide susceptibility maps (LSM) were produced using two models, 9 from fuzzy logic and 1 from frequency ratio. All the results were verified spatially and statistically using landslide locations and ROC curves. Further, the performance and significance of different outputs were compared to select the most suitable LSM for the study area. Among all fuzzy operators, “gamma” with λ = 0.9 showed the best accuracy (84.3%) and operator “and” has the worst accuracy (77.6%). But among all 9 output maps of fuzzy logic except the output of gamma (λ = 0.9) gives satisfactory LSM rest all show the unacceptable result as the maximum number of pixels is either in very low or high susceptible zone. The validation and comparison result exhibited that the fuzzy logic (accuracy=84.3%) is better than the information value (83.46) and the frequency ratio method (accuracy=83.43%).
Keywords: Bivariate Statistical Techniques, Information Value, Frequency Ratio, Fuzzy Logic, ROC
How to cite: Sweta, K. and Goswami, A.: Landslide Susceptibility Zonation Mapping in and Around Dharamshala, Himachal Pradesh Using Bivariate Statistical Techniques – A Comparative Study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21567, https://doi.org/10.5194/egusphere-egu2020-21567, 2020.
Identification of complex surficial and internal sedimentological characteristics of landslide deposits can provide insights into the emplacement mechanisms of mass movements. In this study, deposits of the Tsugaru-Juniko landslide, which was historically recorded triggered by an earthquake in 1704 (Imamura, 1935), in Aomori Prefecture, Japan were investigated. This landslide extended about 2 km from east to west with a volume of about 108 m3 (Furuya et al., 1987), of which deposit is represented by irregular topography and several lakes on and around the rim of it. We conducted field geological and geomorphological surveys and made geomorphological and geophysical analyses using a 1-m resolution LiDAR-DEM and 2D electrical resistivity tomography (ERT) measurement (10 m spacing of electrodes) over a 450 m wide landslide deposit. In plain view, the landslide deposit exhibits quite different features between its northern and southern parts, and each shows a clear sequential distribution of various features. At the northern part, the translation zone is characterized by hummocks and debris lobes containing mixtures of poorly sorted, angular, blocky rock debris of andesitic tuff. Prominent features on the debris lobes are debris-flow-ridges with lobate-shaped aprons extending NW to the downslope. In the accumulation zone, slope surface upheavals of compression origin and radial cracks are observed in the front part of the landslide. At the southern part, as compared to those features observed at the northern part, the slope is commonly marked by transverse ridges, oriented NE-SW, with prevalent steep cliffs on both sides, but generally steeper on the east. The ridges are separated from one another by trenches, elongated across the slope. Based on the distributions of these features, possible explanations on the formative processes of the landslide are complex associated with flowing and sliding at northern and southern parts, respectively. However, geological evidences from its internal structures are rare, ERT survey at the northern part of the landslide deposit reveals that up to 30-m-deep high-resistivity anomaly is associated with the landslide deposit, and low-resistivity anomaly with the bedrock consisting of pumice tuff, as also confirmed in the field. This may result from the high porosity of landslide deposit, because the displaced material deposited loosely.
How to cite: Tsou, C.-Y., Higaki, D., Yamabe, K., Kiru, T., Sasagawa, T., and Numata, S.: Possible explanations on the formative processes of the Tsugaru-Juniko landslide, northern Japan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6609, https://doi.org/10.5194/egusphere-egu2020-6609, 2020.
Between mid-March and the beginning of April 2019, extremely high precipitation affected the whole Iran, leading to widespread flash flooding and landslides. Approximately 10 million people were affected, among them 2 million were in humanitarian needs. The event caused 78 fatalities, more than 1000 injuries and widespread damage in 25 out of the 31 provinces.
In this work, we use both high resolution – spatial and temporal – optical and radar satellite remote sensing to characterize spatiotemporal pattern of landslide occurrence related to the main hydro-meteorological triggering events in Golestan province, North Iran. Large-area landslide detection has been performed in a semi-automated way using time series of optical Planet Scope and Sentinel-2A/B data. The obtained satellite remote sensing based results were evaluated by field surveys conducted in September 2019 in cooperation between the GFZ Potsdam and the Forest, Range and Watershed Management Organization of Iran (FRWM) being responsible for landslide hazard and risk assessment as well as the design and implementation of mitigation measures.
Moreover, we report on our deformation monitoring using Sentinel-1/B based differential interferometric synthetic aperture radar (DInSAR) on hot-spots areas to investigate whether any of the catastrophic landslides that happened in spring of 2019 have shown precursory signs in form of preparatory deformation. In particular, we present our detailed investigation for Hossein Abad Kalpush landslide, located at the border between Golestan and Semnan provinces. In April 2019, this slide slipped at an unprecedented scale, causing total destruction of one part of the village nearby with complete destruction of 250 houses. Using an integrated approach exploring satellite imagery, in-situ measurements and field survey, we perform detailed time-series analysis of the evolution of Hossein Abad Kalpush landslide and examine the role of meteorological and anthropogenic influencing factors in controlling the behaviour of this landslide.
How to cite: Motagh, M., Roessner, S., Akbari, B., Behling, R., Stefanova Vassileva, M., Haghshenas-Haghighi, M., and Ulrich-Wetzel, H.: Landslides triggered by 2019 extreme rainfall and flood events in Iran: Results from satellite remote sensing and field survey, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10715, https://doi.org/10.5194/egusphere-egu2020-10715, 2020.
The stability and lifetime of construction projects in mountain areas are strongly dependent on local slope activity. Hydropower projects in particular are often affected and endangered by landslide damming and flood wave generation due to slope failures, and thus extensive studies of ground surface instability are vital to assess associated hazards. The Rogun Hydropower Project HPP located in Tajikistan in the Vakhsh – Surkhob River network is currently under construction. The site lies within the seismically active Tien Shan and Pamir Mountain ranges of Central Asia and in particular the Peter the First Range. This region is marked by extreme topography, steep slopes and extensive valley networks and has experienced large and catastrophic slope failures in the past, of which a multitude have been triggered by earthquakes of magnitude M≥4. Co-seismic failures are thus common in the area and present a high geotechnical hazard; however, to date no information on active slope instabilities in its catchment area exists.
Here we present an inventory of slope instabilities in the Rogun Dam catchment area based on optical and synthetic aperture radar differential interferometry (DInSAR) remote sensing techniques. Sentinel-1 multi–temporal differential interferograms are generated for summer periods of 2016 – 2018 to detect surface displacements. Slope velocities are estimated based on a comparison between differential interferograms, while landslide types are identified based on a geomorphological classification. A likelihood analysis is developed to understand the state of activity of slopes and provide a semi-quantitative confidence thereof. The collected data is subsequently integrated to perform spatial and statistical analyses in order to perform a proximity analysis, assess a co-seismic link and evaluate the damming hazard potential to the Rogun HPP. Results show that a clear majority of detected features are located within 10 km of major faults and in zones of high peak ground acceleration, indicating a potential seismic influence or triggering. Some active slopes show an increase in surface displacement after a particular earthquake event and equally suggest a potential link. Moreover, we developed a damming hazard analysis for slopes detected as active in Sentinel-1 differential interferograms, considering the likelihood of movements, their distance to rivers and faults, as well as estimated volume and velocity per year. The results indicate that a total of 29.6 % of all features constitute a high damming hazard potential in case of catastrophic failure, with 4.5 % located within 1 km of the Rogun Dam reservoir. Although many potential sites are not directly on the slopes rising above the future reservoir, hazardous locations in the catchment upstream pose a threat due to possibility of significant outburst floods in case of the dammed lake outburst.
How to cite: Jones, N., Manconi, A., and Strom, A.: Slope Activity Analysis in the Rogun Catchment Area, Tajikistan, using Remote Sensing Techniques, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6663, https://doi.org/10.5194/egusphere-egu2020-6663, 2020.
High-resolution DTM does not always help build a good landslide prediction model. When we are using LiDAR DTM in producing a topographic-related factor for grid-based landslide susceptibility/hazard analysis, the selection of an optimal measurement scale becomes important. Because the resolution of LiDAR DTM may be up to 1 meter, and the average landslide size may be more than 1 thousand square meters, to use a conventional 3x3 kernel for calculation of a factor value is not valid. Actual tests tell us, to use a 15x15 and larger kernel for calculation may yield a more effective factor for interpreting the landslide distribution in a study area.
A test area was selected at the catchment of the Zengwen Reservoir in southwestern Taiwan. The original 1mx1m LiDAR DTM was firstly reduced to a 2mx2m DTM for analysis. Factors of slope gradient, slope aspect, topographic roughness, slope roughness, plan curvature, profile curvature, tangential curvature and total curvature are analyzed by using a series of kernels in different sizes up to 25x25 for comparison. And success rate curve method was used to evaluate the effectiveness of each factor in interpreting landslide distribution. Highest AUC is selected as the most effective one and the kernel size which yield that is the optimal measurement scale of the factor.
A 3x3 kernel has a measurement scale of 2h and is 4 meters (h is grid size of 2 meters), a 25x25 kernel has a measurement scale of 24h and is 48 meters. Factors calculated from an optimal measurement scale will be selected for construction of a landslide susceptibility model. The success rate and prediction rate of this model would be significantly increasing as compared with the model built from conventional 3x3 kernel calculated factors. Finally this optimal susceptibility model was used to construct a landslide hazard model for prediction of landslide distribution under different triggering events.
How to cite: Lee, C.-T. and Ji, T.-C.: The Use of LiDAR DTM in Landslide Susceptibility/Hazard Analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7694, https://doi.org/10.5194/egusphere-egu2020-7694, 2020.
More than a century ago, the Puigcercós village located in the region of Pallars Jussà (Catalonia, Spain), suffered a large-scale landslide that occurred on January 13th, 1881. More than 5 million m3 of sediments and rocks were displaced and a 200 m long and 25 m high rock scarp was formed. Luckily, during the main event, the nearby village was not affected, and due to a prompt evacuation and re-location of the entire village, no casualties were reported. Nevertheless, consequent retreat of the main scarp did destroy the big part of the old village, which confirmed not only the necessity for its relocation, but also gave one of the first clearly described and confirmed examples of a successful geologic risk prevention.
During the last decade, the members of the RISKNAT-UB group have chosen this site to conduct pilot studies of rockfalls and landslides using a multidisciplinary approach. The utilized observational techniques include Terrestrial Laser Scanner (TLS), photogrammetry, GPS, seismic monitoring and geophysical prospecting techniques. The work presented here is an overview of these activities, including the main milestones of the ongoing research. Special emphasis will be given to the use of geodetic techniques for investigating changes on the depositional area of the landslide and around the crown cracks at the upper level of the main scarp. As a result of the GPS observations, for the first time, 130 years after the occurrence of the event, it was possible to observe a continuing geomorphological activity of the depositional zone of this historical landslide, Currently, the RISKNAT-UB group operates cost-effective, high-resolution and low-cost photogrammetric instruments and seismic continuous records at the site, in order to monitor the evolution of the Puigcercós rock scarp. The correlation of the seismic and the photogrammetric data and intermittently obtained LiDAR images enables us to monitor and characterize frequent rockfalls and premonitory deformations occurring at the site. These observations have allowed quantifying the rate of retreat of the rock scarp at a rate of 10 to 11 cm/yr and a slow motion of the depositional zone up to 6 mm/yr. Since the geologic risk at the study area is not significant, due to the absence of population and/or infrastructures, this site is an ideal natural laboratory for developing new observational techniques, which can be used to develop early warning systems for rockfalls and landslides.
The authors would like to acknowledge a financial support from CHARMA (CGL2013-40828-R) and PROMONTEC (CGL2017-84720-R AEI/FEDER, UE) projects, financed by the Spanish MINEICO. We are also thankful to UNESCO Global Geopark Conca de Tremp-Montsec for their support.
How to cite: Khazaradze, G., Guinau, M., Blanch, X., Abellán, A., Tapia, M., Furdada, G., and Suriñach, E.: Multidisciplinary studies of the Puigcercós historical landslide in the Catalan Pyrenees, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7796, https://doi.org/10.5194/egusphere-egu2020-7796, 2020.
Machine learning has proven most effective in mapping landslide susceptibility. We carry out experiments with two machine learning algorithms, SVM and MaxENT to study their effectiveness for some mountaneous areas in Pakistan. A data set of 112 historic landslides are used in the study with 70% of the landslides are used for training and the rest for validation. 15 landslide casuative factors are used initially and ineffective ones are eliminated based on information Gain Ratio and Multicollinearity test techniques. The perfromances of the landslides susceptibility maps generated are assessed using receiver operating curves (ROC), confusion matrix (CM) (Kappa, root mean square error, mean absolute error and balanced accuracy), landslide density (LD), R-index and Pearson’s Chi-squared tests. The result show that both of the models work well in this area. However, the lowest significant value ‘p’ (<0.05) during Chi-square test, showed that both the landslide models have statistical significant difference.
How to cite: Shahzad, N., Ding, X., and Abbas, S.: Prediction ability of machine learning algorithms in Himalaya region of Pakistan for landslide susceptibility mapping, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6757, https://doi.org/10.5194/egusphere-egu2020-6757, 2020.
This study used synthetic aperture radar interference technology (InSAR) to monitor the activities of large-scale collapse zones in southern Taiwan (Tainan City, Kaohsiung City, Pingdong County). Large-scale collapse zones are widely distributed, in addition to the construction of observation instruments, how to use other telemetry technology to quickly obtain relevant change information as monitoring and early warning indicator is a vital issue. SAR images from southern Taiwan from 2015 to 2019 were analyzed to monitor the ground surface changes using synthetic aperture radar differential interference technology (DInSAR) and permanent scattering interferometry radar technology (PSInSAR), and were verified using global navigation satellite system measurements. DInSAR analysis shows that the vertical displacement of the surface is ±60mm, which is within the range of elevation tolerance error, so it is not possible to use the satellite tracking station to compare the trace displacement in large collapse areas. However, PsInSAR results show that if there is PS point in a large-scale collapse zone, the PS point may be used as index of stabilization, and once the PS point suddenly disappears, it is highly likely that the area will change, and special care should be taken.
Keywords: Interferometric SAR, large-scale collapse zones, PSInSAR
How to cite: Wu, J. P. and Lin, C. Y.: Activity Tracking and Evaluation of Large-scale Collapse Zones using Synthetic Aperture Radar Differential Interferenc, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7965, https://doi.org/10.5194/egusphere-egu2020-7965, 2020.
Landslide is one of the major geohazards in the Three Gorges area as a result of steep valley-side slopes and environmental conditions, e.g., high precipitation. To monitor and detect the landslides and rock falls at a regional scale as Three Gorges area, the differential Synthetic Aperture Radar Interferometry (D-InSAR) technology could be more effective and efficient than other conventional geological and geodetic measurements that can be performed only at a few sites with proper accessibility and conditions.
Over the past few decades, InSAR technology and advanced SAR Interferometry techniques such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subsets (SBAS) have been developed to derive ground displacement over large areas with high-resolution measurement points and acceptable accuracy (cm to mm level). Both PSI and SBAS methods are based on a network of coherent pixels, including natural persistent scatterer (NPS) and artificial corner reflector (CR). NPSs can be easily found in urban areas or rocky regions. However, for landslide monitoring, the NPSs are usually difficult to be identified due to the steepness, vegetated and vulnerable moisture content among the high-risk locations. In this work, multiple SAR datasets including C-band Sentinel-1, L-band ALOS-2 and X-band TerraSAR-X (TSX) are exploited for landslide monitoring along the Yangtze River in the Three Gorges area in China. Both PSI and SBAS methods are utilized. Besides, stable artificial CRs are deployed on selected sites to evaluate their performance in deriving landslide kinematics. Results are presented and discussed for a better assessment of landslide hazards in the Three Gorges region.
How to cite: Xia, Z., Motagh, M., and Li, T.: Landslide Monitoring by Integrating Multi-Sensor InSAR Time Series Datasets and Corner Reflectors in the Three Gorges Area, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20159, https://doi.org/10.5194/egusphere-egu2020-20159, 2020.
Detection of slope instability using Interferometric Synthetic Aperture Radar (InSAR) can aid the understanding of landslide kinematics and prevent the related geological hazards. However, conventional InSAR techniques often fail in the retrieval of deformation measurements in mountainous areas with dense vegetation and complex terrain, thus resulting in diminished information of slope movement. In this study, we propose a new multi-temporal InSAR method to improve the spatial coverage of measurement points by jointly exploiting persistent scatterers (PS) and distributed scatterers (DS). Particularly, topographic errors and tropospheric delays are well-considered according to their spatial and temporal characteristics. We applied this method to retrieve the historic displacements prior to the collapse of an artificial slope in Northern Taiwan using 15 ALOS/PALSAR images. The derived results suggest a pre-landslide movement with a rate of approximately -30 mm/year in the radar line-of-sight (LOS) direction. Meanwhile, the time series displacements reveal that the temporal behaviors of downslope movement are correlated with local rainfall and seismic activities. The study helps to analyze the slope instability in Northern Taiwan.
How to cite: Liang, H., Zhang, L., and Ding, X.: Investigation of active movement prior to artificial slope landslides from multi-temporal InSAR: a case study of Northern Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9050, https://doi.org/10.5194/egusphere-egu2020-9050, 2020.
Continuous INSAR-monitoring of slow mass movements in the surrounding of fast (m/year) or acute processes can deliver important data complementing geomorphologic information in order to understand the broader dynamic context in which a landslide is situated. In course of the Landslide-EVO project (NERC/SHEAR funded), focusing on flood and landside risk assessment and mitigation in the Karnali river basin region in Far Western Nepal by inclusion of local community, this has been evaluated within a test of integrated monitoring methods (comprising eg. ERT, UAV-photogrammetry, D-GPS/geodesy, microseismics, soil water saturation, rainfall, and other) on regional as well as local scale at two selected sites at Bajura and Sunkoda. It was possible to derive extended information about movements in a ROI covering 120 km by 120 km. The PSI/SBAS based velocity analysis exhibits density variations due to specific slope/sensor system geometry, vegetation, data gaps, atmospheric conditions, and high velocities in the most active sites, which causes decorrelation. However, in the less active surrounding of active landslides the velocity information shows generally higher density. INSAR techniques could well complement optical image analysis in the low velocity range of centimetres to several decimetres per year, generally too slow for optical satellite image analysis in this time scale. InSAR-data has the potential to be used for estimating a slow moving masses acceleration or a deep-seated gravitational slope deformations cumulative displacement leading to a partial or total reactivation before other indication appears. It has been shown that large and difficult accessible areas can be monitored with InSAR techniques, while specific sites are equipped with corner reflectors for better signal. The study represents the first of this kind in the region and proves the ability of INSAR techniques for retrieving critical information about mass movements affecting local communities in the Karnali river basin as an example of a developing region.
How to cite: Schiller, A., Vecchiotti, F., Amabile, A. S., Guardiani, C., Dhital, M. R., Dhakal, A., Pant, B. R., Ostermann, M., and Supper, R.: Ground motion and PSI density analysis from Envisat and Sentinel1a InSAR data in the context of a complex landslide monitoring strategy in Karnali river basin, Far-Western Nepal, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21875, https://doi.org/10.5194/egusphere-egu2020-21875, 2020.
In the year 2019, at Kartais (Hüttschlag, Austria) parts of an approximately 100 m high and fractured rock wall mainly composed of calcareous-mica-schists became unstable and collapsed two times. The first failure event was a wedge failure and occurred on the 25th of March 2019 and released about 3.000 m3 of rock material. Blocks with a maximum volume of about 100 m3 were falling, bouncing and sliding to the valley bottom, but did not reach the Großarl River and the local infrastructure (road, bicycle track and houses). The second failure event happened on the 15th of July 2019 involving a volume of about 5.000 m3 with a maximum block size of 200 m³. This event had a longer runout but also did not reach the infrastructure. A Helicopter-based observation by the Geological Survey of Salzburg has shown that new cracks at the top of the failure area have already opened to apertures in the scale of decimetres to metres. It is assumed that the newly formed potential failure mass could reach 10.000 m³ and thus is even larger than the two previous events. In order to study the deformation behaviour of the rock face a multi-methodical observation and monitoring campaign has been initiated recently. A UAV-photogrammetry survey has shown that the foliation of the calcareous-mica-schist is dipping moderately into the slope and the rock wall is dissected by at least 4 different joint sets, whereas two of them intersect to form wedge failures. Since November 2019 a GBInSAR system (LisaLab) is continuously monitoring the slope. Additionally, multi-temporal terrestrial laserscanning (TLS) surveys and satellite based InSAR analysis were performed.
In this contribution, the set-up of the investigation and monitoring campaign as well as some preliminary results will be presented.
How to cite: Amabile, A. S., Kuschel, E., Ostermann, M., Vecchiotti, F., Straka, W., Koçiu, A., Valentin, G., and Zangerl, C.: A multi-methodical approach based on GBInSAR, Satellite InSAR, and terrestrial Laserscanning for the investigation and monitoring of an unstable rock slope, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20693, https://doi.org/10.5194/egusphere-egu2020-20693, 2020.
Ground displacements associated to landslides can be analysed by means of geological, geotechnical, topographic and remote sensing techniques. In this work different classical topographic techniques are combined with a satellite based remote sensing technique: Differential SAR Interferometry (DInSAR). The topographic techniques provide precise measurements on a set of points strategically located for each landslide. The DInSAR technique provides a more opportunistic set of points, usually denser than topographic techniques, providing key information on the area of influence of the movement and its potential impact on the surroundings. The combination of both approaches provides a complementary set of measurements useful to properly understand the landslide mechanics. The area of study is Tazones Lighthouse sector (43º 32’ 54’’N, 5º 23’ 57’’W), located on a coastal cliff in north Asturias (N Spain), where there is an important active mass movement.
The used procedure consisted in the following steps: a) Processing of Envisat ASAR satellite data from 2002 to 2012 to obtain the deformation velocity map of the zone of interest thorough the ESA G-POD service (European Space Agency Grid Processing On Demand); b) Processing of the period 2014-2019 with Sentinel-1 data to obtain the Deformation time series and the deformation velocity map with the PSIG software (developed by the Geomatics Division of the CTTC); c) Integration, combination and comparison by a Geographical Information System (GIS) of the satellite results with topographic data obtained from 2018 to 2019 by means of standard techniques (theodolite, feno survey markers and control points); d) Analysis and interpretation of the results taken into account geological-geomorphological data available.
The results of this study show different velocity ratios in the Area of Interest (AoI), from mm/year to m/year, which are consistent with the ground measurements. Therefore, the work demonstrated the potentials of combining different geodetic techniques to infer information about landslides processes and the usefulness of the DInSAR for the control of the mass movement, whose fast evolution makes it difficult the topographic work due to the changes in the relief and the loss of several feno survey markers.
How to cite: Cuervas-Mons, J., Monserrat, O., Domínguez-Cuesta, M. J., Mateos-Redondo, F., González-Pumariega, P., López-Fernández, C., Valenzuela, P., Barra, A., Pascual-Lombardía, P., and Jiménez-Sánchez, M.: DInSAR and topographic techniques applied to study the Tazones Lighthouse landslide (N Spain), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10455, https://doi.org/10.5194/egusphere-egu2020-10455, 2020.
Landslides are recurrent in most mountainous areas of the world where they frequently have catastrophic consequences. Around the Fergana Basin and in the Maily-Say Valley (Kyrgyzstan), landslides are often reactivated due to intense rainfalls, especially during spring, and as a consequence of the high seismicity characterizing the region. In spring 2017, Kyrgyzstan suffered a massive activation event which caused 160 emergency situations, including the reactivation of Koytash, one of the largest deep-seated mass movements of the Maily-Say area. In this region, risks related to landslides are accentuated by the presence of uranium tailings, remnants of the former nuclear mining activity. In this study, we used multiple satellite remote sensing techniques to highlight deformation zones and identify displacements prior to the collapse of Koytash. The comparison of multi-temporal digital elevation models (DEMs; satellite and UAV-based) enabled us to highlight areas of depletion and accumulation, in the scarp and foothill zones respectively. A differential synthetic aperture radar interferometry (D-InSAR) analysis and the computation of deformation time series allowed us to identify slope displacements and estimate the evolution of the displacement rates over time. This analysis identified slow displacements during the months preceding the reactivation, indicating the long-term sliding activity of Koytash, well before the reactivation in April 2017. This was confirmed by the computation of deformation time series, showing a positive velocity anomaly on the upper part of Koytash. Furthermore, the use of optical imagery, through the difference of NDVIs (Normalized Difference Vegetation Index), revealed landcover changes associated to the sliding process. In addition to remote sensing techniques, we performed a meteorological analysis to identify the conditions that triggered the massive failure of Koytash. In-situ data from a local station highlighted the important contribution of precipitations as a trigger of the landslide movement. Indeed, despite a relative decrease in annual rainfall in 2017 compared to the previous years, the month of April 2017 was characterised by heavy rains, including a major peak of rainfall the day of Koytash’s failure. The multidirectional approach used in this study, demonstrated the efficiency of using multiple remote sensing techniques, combined to a meteorological analysis, to identify triggering factors and monitor the activity of landslides.
How to cite: Piroton, V., Schlögel, R., and Havenith, H.-B.: Monitoring recent activity of the Koytash Landslide (Kyrgyzstan) using radar and optical remote sensing techniques, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20180, https://doi.org/10.5194/egusphere-egu2020-20180, 2020.
Many regions of the world are exposed to landslides in clay deposits, which poses major problems for land management and population safety. In recent years, optical satellite imaging has emerged as a major and inexpensive tool for understanding and monitoring the kinematics of slow moving landslides, such as earthflows/earthslides, through easy access of data and reliable calibration.
The Sentinel-2 optical satellites provide a global coverage of land surfaces with a 5-day revisit time at the Equator. We studied the ability of these freely available optical images to detect landslide reactivations in a zone of 25 km2 around the Harmalière landslide in the Trièves area (western Alps, France). This area is characterized by the presence of a thick lacustrine clay layer that is affected by numerous landslides. Using a 9-month time-series of displacement derived from Sentinel-2 data, Lacroix et al. 2018 recently evidenced a precursor displacement of a major reactivation of the Harmalière landslide that occurred in June 2016.
In this study, we attempted to detect following reactivations using the medium resolution high frequency satellite images (Sentinel 2) coupled with high resolution images (Pléiades) over a longer period (2016- 2019). We used an inversion strategy of redundant cross-correlation images to produce a robust time-series of displacement from Sentinel 2 data (Bontemps et al. 2018). By applying this technique, we were able to identify a reactivation of the same order of magnitude as the previous one, which affected the headscarp in January 2017. The reactivation signal is validated by the cross-correlation of Pléiades images taken at 2 years interval. We quantified this reactivation in time and space. We have also identified an area of 30x103 m2 located at the foot of the landslide, which was simultaneously accelerated by 10 m/month during this event. This information contributes to better understand the dynamics of the landslide that evolves from a solid to fluid behavior from the headscarp to the toe. However, a smaller slide that occurred in January 2018 at the headscarp was not detected by this method despite its significant size (10x103 m2). We attribute this non-detection to a major reshaping of the surface following reactivation.
This study identified the possibilities and limitations of the proposed treatment method to detect and monitor landslides on a low-slope area located in clayey soils in a temperate climate.
Bontemps, N., Lacroix, P. & Doin, M.-P. (2018) Inversion of deformation fields time-series from optical images, and application to the long term kinematics of slow-moving landslides in Peru. Remote Sensing of Environment, 210, 144–158. doi:10.1016/j.rse.2018.02.023
Lacroix, P., Bièvre, G., Pathier, E., Kniess, U. & Jongmans, D. (2018) Use of Sentinel-2 images for the detection of precursory motions before landslide failures. Remote Sensing of Environment, 215, 507–516. doi:10.1016/j.rse.2018.03.042
How to cite: Jongmans, D., Fiolleau, S., Bièvre, G., Chambon, G., and Lacroix, P.: Combination of high frequency (Sentinel-2) and high resolution (Pléiades) satellite images for the monitoring of clayey landslide reactivations, application to the Harmalière landslide (French Alps)., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9368, https://doi.org/10.5194/egusphere-egu2020-9368, 2020.
Surface soil moisture is recognised as an important measurement for use in the assessment of potential slope instability in hydraulically driven landslides. In this poster we present a nine month time series of surface soil moisture estimates derived from ESA’s Cosmo SkyMed Synthetic Aperture RADAR (SAR) product at the Hollin Hill Landslide Observatory in North Yorkshire, UK.
We show the relationship between these SAR-derived SM values and ground-truthed surface soil moisture data, explore spatial relationships between areas of high soil moisture and landslide activity and briefly discuss the potential of SAR data as an input for Landslide Early Warning systems.
How to cite: Bliss, T., Wainwright, J., Donoghue, D., and Jordan, C.: Estimating soil moisture from COSMO-SkyMed data at an active landslide site in North Yorkshire, UK, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22063, https://doi.org/10.5194/egusphere-egu2020-22063, 2020.
The European Space Agency’s Geohazard Exploitation Platform (GEP) (https://geohazards-tep.eu/#!) is a web-based platform through users can perform independent analysis by exploiting satellite data. This platform hosts several thematic apps that allow to identify, monitor and asses hazard related to geological processes such as volcanism, land subsidence or landslides. The Sentinel-1 CNR-IREA SBAS service is one of these thematic apps that consists on a Differential SAR Interferometry (DInSAR) processing chain for the generation of Earth deformation time series and mean velocity maps of surface ground displacement. In the last decades, DInSAR techniques have proved to be powerful tools to detect and monitor active processes related to geological ground instability issues. In this context, the Sentinel-1 GEP service seems to be a promising way to perform independent and high temporal resolution DInSAR analysis from any part of the world in just 24 hours.
At present time, GEP continues being fine-tuned and users are working to validate the obtained results by comparing them with other data. In this way, it is possible not only to evaluate the advantages and limitations of the platform and but also to acquire new information about geological active processes around the world. In this work, we present an overview of different locations in the Mediterranean Basin and northwestern South America where we are accounted for previous knowledge of active landslide activity. Where there was previous InSAR analysis, we compared recent InSAR velocity maps with displacement rates that we obtained by the Sentinel-1 CNR-IREA SBAS tool to check their reliability. Moreover, we explored areas with no previous monitoring information but field evidence of ground instability. Beyond this, we considered this service as a successful tool to perform preliminary analyses of Sentinel-1 images in non-investigated areas to spot hazards and to delimit zones for performing detailed investigations. Additionally, some other unsatisfactory results allowed us to draw conclusions about technical constrains of the GEP tool and further asses its usefulness.
This work has been developed in the framework of the RISKCOAST project, founded by the Interreg SUDOE program.
How to cite: Reyes-Carmona, C., Galve, J. P., Barra, A., Monserrat, O., Mateos, R. M., Azañón, J. M., Pérez-Peña, J. V., and Ruano, P.: The Sentinel-1 CNR-IREA SBAS service of the European Space Agency’s Geohazard Exploitation Platform (GEP) as a powerful tool for landslide activity detection and monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19410, https://doi.org/10.5194/egusphere-egu2020-19410, 2020.
With the combination of diverse remote sensing data, one can estimate the detection capabilities of gravitational mass movement dynamics and behaviour. Recent multispectral satellite sensors such as Sentinel-2, RapidEye and PlanetScope offer unprecedented spatiotemporal resolutions, hence reducing data gaps of alpine meteorological constraints. In addition to this data, very high resolution and accurate UAV images cover a broad range of spatial resolutions. The strengths of these remote sensing systems allow the data compilation of vast, difficult and dangerous to access mountain areas. However, the limitations of the spatiotemporal resolution for (i) pre-event landslide detection, (ii) monitoring of already known mass movements and (iii) the capability to measure rapid changes (e.g. accelerations) for warnings have not been examined extensively. Thus, there is an important need to understand the potential of multispectral images to detect, monitor, and identify rapid changes prior to landslide events to increase the forecasting window.
Digital image correlation (DIC), as indispensable tool to measure surface displacements, aids in estimating the fitness of different remote sensing images. Here, we present first results of motion delineation by DIC of the Sattelkar, a high-alpine, deglaciated and debris-laden cirque in the Obersulzbach-valley, Austria. We used comprehensive knowledge of the study site to thoroughly understand DIC motion clusters for verification purposes. We then compared three different DIC software tools, COSI-Corr, DIC‑FFT and IMCORR. They revealed similar results for the three satellite systems in terms of hot spot areas as well as noise. Our findings show large motion inaccuracies for Sentinel-2, RapidEye and PlanetScope images due to spatial resolution, poor image co-registration and changing data quality. In contrast, displacement patterns from the three UAV images (7/2018, 7/2019, 9/2019) demonstrate good positional accuracy as well as data usability for this approach. The inherited noise results from decorrelation due to high velocities suggest using an increased temporal image acquisition for further evaluation.
Reliable, precise results for landslide detection, their ongoing monitoring and the measurement capability for significant changes are necessary for targeted investigations, precautionary measures and the start of the forecasting window. Multispectral UAV images of high positional accuracy and quality are able to provide dependable relative displacement velocities and have the capability to serve as a reliable tool. On the contrary, satellite images showed delusive results, and we recommend reconsidering their deployment in future applications. The knowledge of the most suitable data in terms of accuracy and processing speed is crucial for landslide identification, monitoring and acceleration threshold detection. At present, our prelimiary findings show the capability to detect and monitor relative and mainly slow changes. The detection of rapid changes lacks due to the accuracy, resolution and revisit time of the investigated remote sensing systems.
How to cite: Hermle, D., Keuschnig, M., and Krautblatter, M.: Potential of multisensor assessment using digital image correlation for landslide detection and monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16982, https://doi.org/10.5194/egusphere-egu2020-16982, 2020.
UAS have been increasingly utilized for research in Natural Hazards and Risk Management, especially when it comes to inaccessible study areas where the thorough examination of the existing geological-tectonic structures cannot be achieved only by field work. The study area is located on Chios island (North Aegean Sea, Greece) along the Chios-Kardamyla Road in the region from Mersinidi to Myliga, where the particular geodynamic and seismotectonic regime results in earthquakes which cause a great amount of natural disasters including many landslides. The largest part of the area was inaccessible. The use of SfM (Structure for Motion) techniques to obtain data from the UAV (Unmanned Aerial Vehicle-DJI Mavic Pro) flights above the study area led to detailed phototopographic, photomorphological, photogeological-tectonic and photogeotechnical mapping, detailed boundary and surface tectonic mapping and high-accuracy structural analysis in 3D environments. The combination of field work and UAS-based photogrammetry, provided complete and reliable results by following rapid and low-cost procedures by using Pix4D, ArcGIS, Rockware Rockworks 17, Rocscience Rocfall, Rocscience Slide and CAD software. The methodology was developed on the outline of the following workflow:
- Evaluation of existing geological, geotectonic, hydrogeological, seismotectonic and geotechnical data
- Flight project planning, according to: equipment specifications and capabilities, requirements of visual analysis, extent and morphology of the study area and expected weather conditions.
- Field mapping and UAS flight execution (imagery and footage capture).
- UAS imagery processing and interpretation: production of 3D models, Digital Surface Models (DSM), Digital Terrain Models (DTM) and Orthomosaics, formation boundaries recognition.
- Production of Geological-Tectonic maps for the study area.
- Research of the discontinuous tectonic deformation (SfM recognition and 3D mapping of tectonic lines and surfaces). Extraction of tectonic data (direction, dip, dip direction, aspect etc).
- Field and SfM tectonic data analysis and statistics (unification of tectonic data archive, weighting of the statistics, statistical processing and diagrams – density, rose, cyclographic projections etc).
- Research of the hydrogeological conditions of the area (determination of the role of groundwater in rock and soil movements according to hydrolithology and tectonic texture).
- Geotechnical mapping and hazard assessment.
Furthermore, this study includes the identification of the slope failures and the rock mass classification according to the internationally accepted stability calculation methodologies. Specific plans for rockfalls and rock slides, analysis of rockfall evolution and detailed simulation models of rockfalls were extracted. Appropriate measures and proposals for landslide risk reduction projects were also made. The evaluation of drilling results along the study area, the causes of landslides, the slope stability calculations and the proposed countermeasures are presented in the research. Especially regarding the carbonate rocks in the area, they have undergone tectonic strain that has led to their fragmentation into blocks and boulders. In combination with the water activity which reduces the shear strength of the discontinuities and the friction between a) the carbonate blocks and b) the carbonate mass and the clastic basement, these rock blocks are easily detached to overturn or slide on the downhill slopes, during intense precipitation or earthquake phenomena.
How to cite: Stanota, E. S., Spyrou, N. I., Andreadakis, E., Skourtsos, E., Lozios, S., and Lekkas, E.: Landslide Behaviour and Risk Reduction using SfM and 3D modelling techniques with Unmanned Aerial Systems (UAS). Chios island (Greece)., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18852, https://doi.org/10.5194/egusphere-egu2020-18852, 2020.
The efficiency of linear infrastructure influences heavily the social and economic development of a territory; hence the assessment of pavement damage is of major interest for local authorities when planning road maintenance in landslide affected areas to ensure the safety of its users. Ground movements related to landslides, subsidence and earthquakes are common causes of pavement deterioration, other than usual traffic stress conditions. Major issues for the assessment of landslide impacts on transportation routes remain the quantitative and objective description of the typology and extent of pavement damage, and its classification, with the aim to correlate the damage with the nature and intensity of the causing phenomena. This work investigates the use of three-dimensional models reconstructed from UAV based digital photogrammetry, as a rapid and less laborious alternative to the traditional field surveys, for assessing the damage induced by slow-moving landslides interacting with linear infrastructure. A semi-automatic procedure is proposed to rapidly detect and quantitatively describe the damage on asphalt-paved roads affected by slow-moving landslides. The methodology includes the processing of the 3D points cloud models using edge detection algorithms and roughness estimations to detect pavement anomalies. Damage assessment using the proposed methodology allows to i) automatically extract the geometric features of road damage, ii) measure objectively fractures and/or deformations of the road pavement and iii) create a damage database using geolocation data. The procedure is applied to road tracks located within slow-moving landslides and tested using RGB images taken from a Phantom 4 drone flight at 30m and 10m altitudes from the road surface and field measurements. The proposed methodology for semi-automatic road damage detection can contribute to the improvement of landslide risk analysis and mitigation for road networks affected by ground movements.
How to cite: Nappo, N., Mavrouli, O., van Westen, C., Gambillara, R., and Michetti, A. M.: Semi-automatic road damage detection in landslide areas using UAV-based 3D models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5910, https://doi.org/10.5194/egusphere-egu2020-5910, 2020.